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\chapter{Introduction - The Heartbeats of Social Networks}

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\settowidth{\versewidth}{I have observed that love begins }
\begin{verse}[\versewidth] 
\sffamily 
I am resolved to keep afar \\
Wherever Love's attractions are; \\
The man of sense, as I detect, \\
Is ever shrewd and circumspect. 

I have observed that love begins \\
When some poor fellow for his sins,\\
Thinks, it is thrilling, ever so,\\
To gaze on cheeks where roses glow.

But while he sports so joyfully\\
With not a care to mar his glee, \\
The links are forging, one by one, \\
And he's enchained, before he's done.

So there he is, deluded fool;\\
Stepping benignly in the pool\\
He slips, and ere he can look round\\
He's swept along the flood, and drowned.


\end{verse}
\attrib{Ibn Hazm Ali Ibn Ahmad (994 - 1064) \citeyearpar{hazm1981}   }


%\section{The Heartbeats of Social networks} % section headings are printed smaller than chapter names
% intro
Written over a thousand years ago, this short poem by the great Muslim polymath, Ibn Hazm Ali Ibn Ahmad of C\'{o}rdoba, makes a couple of interesting observations, setting the stage for the problem addressed in this dissertation. First, the poem makes a distinction between short term interactions and the longer term commitments that bind human beings. The interactions consist of quick gazes, sweet words and innocent exchanges between the lovers, followed willy nilly by the crystallization and consolidation of a persistent bond, the `chains' of a relationship consisting of expectations, responsibilities and obligations. 

The speaker in the poem suggests a (causal\footnote{For a debate on the type of connection between these two levels see \citet{abell2010}}?) connection between these two levels of social associations: short-lived transactions morph into full-fledged ties. But despite their interdependent nature, transactions and ties have very different properties\footnote{\citet{gibson2005} talks about transactions and ties constituting `orthogonal [!]) dimensions of social organization, with [transactions] unfolding sequentially, and social networks [of ties] extending (in a certain sense) spatially.' } in terms of their motivation, their consequences and their longevity. The transactions have a utility\footnote{It is precisely the utility that is associated with social actions, that motivates the use of the word `transaction' in order to describe the social action \citep{elster1989}. More on the usage of nomenclature below and in chapter 2.}  - they contribute to a feeling of joy in the heart of the lover, without bringing about any `care to mar his glee.' But the lightweight and playful nature of these transactions are deceiving, because behind his back, like an evil conspiracy, heavyweight ties are forged and the lovers find themselves ensnared in a system that is already well established, `swept along the flood, and drowned.' No longer are they the agents of their own destiny, no longer do they enjoy the liberty of choice. Now there are other, external powers that are at play, their effect not unlike natural forces, social institutions now guide their actions; the expectations of society, the commitment to a family, the obligation to a career, and a responsibility to lead a structured and secure life. Unawares, the utility functions from the field of economics transform into brute `social facts.' \citep{elster1989}


One last observation relevant to the theoretical discussions (Chapters 3 and 6) is that the speaker warns his readers that the fulfillment of the lover's short term desires  is detrimental to the prospect of his long term happiness. The flip side of the same advice was developed systematically over a thousand years later by the distinguished economist and writer John Kay. In his book \textit{obliquity}, \citet{kay2012} advises his readers to achieve happiness only indirectly, as a side-effect of actions that are motivated by reasons other than the pursuit of happiness. Both Ibn Hazm and Kay challenge a naive relationship between intentions, actions and consequences. Social actions, even if motivated by intention and purpose, bring about unintended consequences, and consequences are attributed ex-post to intentions that never existed. In what follows, I explore the possibility that network structures exist not only thanks to the advantages they bestow on individuals in the network \citep{coleman1988}, but possibly also as a result of a path-dependent evolution, the unintended consequences of transactions that bring short-term benefit to the actors initiating them.  Network inefficiencies (in the Pareto sense) \citep{raub1990} may thus be explained by path-dependency rather than functionalist accounts, ancestral traits rather than adaptive ones. 

Short term transactions and long term ties are two levels that exist in many different systems, not only in the system of the relationship between lovers \citep{luhmann1998}. Consider biological systems such as the cardiovascular system for example, a set of blood vessels, organs and tissue whose primary function is to  provide adequate amounts of oxygen and nutrients to the metabolizing tissues, at the same time ensuring the removal of carbon dioxide and other metabolic waste products \citep{hicks2002}. Here too we can distinguish between a structural system consisting of a semi-static network of arteries, veins, etc. on the one hand, and the actual flow of matter through the system on the other. The cardiovascular network itself is more or less durable, constraining, facilitating and synchronizing the flow of blood according to the needs of the living body. The flow is sustained and regulated by discrete like events such as each and every heartbeat, or the widening and narrowing of blood vessels as a reaction to environmental changes.  

This metaphor provides additional insights; first, the network structure has a functional dimension. By this I mean to say that the reason for its existence is its properties, by virtue of which the living body is sustained. That said, some cardiovascular structures are inefficient and their properties cannot be explained by recourse to their function alone, but rather by a path-dependent, evolutionary process \citep{hicks2002}. Furthermore, this metaphor highlights the notion that though they operate interdependently, the network and the flow are two separately existing phenomena. To demonstrate this, consider the existence of blood vessels of a dead body, after the heart ceases to pump and blood no longer flows. When network structure exists without flow, one can no longer argue that the former is reducible to the latter. It is a matter of debate as to whether or not this metaphor applies to the social realm as well; whether social structure is completely reducible to transactions between human beings or whether these could be conceptually separated, the structure enjoying \textit{sui generis} existence. The question of reducibility of the structure to the flow is an ontological one, a key point in the debate between Durkheimian realism and Tardian nominalism which is discussed in greater detail in Chapters 2 and 6. 

Before demonstrating why the distinction between transactions and ties is relevant for the study of social networks today, I would like to introduce a second metaphor, this time taken from neural networks in the brain. \citet{shalizi2006} distinguish between two types of brain connectivity. The anatomical network consists of persistent, semi-static connections between neurons, bound together through physical nerve fibers in the brain. Another type of connectivity can be defined through coordinated behavior, the network of neurons that tend to synchronize and correlate activity. Even if neurons are connected to one another physically, their activity can be correlated for the accomplishment of one type of task or uncorrelated when accomplishing a different type of task. Thus, the same anatomical structure of neural networks can be mapped to different `functional networks,' depending on the cognitive task at hand. The point again is to emphasize the conceptual distinction between the network as an infrastructure in which flow can potentially take place, and the actual flows, activations or transactions that occur within the network. 

There is really nothing new in the realization that social networks exist as a semi-static structural entity, within which sub-units can be active whereas others are not, exchanging information at different times and for various purposes \citep{mitchell1969}. However, it is fairly recent that systematic work has been carried out with the intention to analyze the temporal structure of network flow and its consequences \citep{holme2011}. A growing body of literature is now examining these issues, revealing new and exciting puzzles. In what follows, I will demonstrate some these issues, highlighting their interdisciplinary nature. 

\section{Transactions and Their Consequences} % subsection headings are again smaller than section names
Traditionally, social networks were used mainly to represent a topological and durable structure of interconnected individual elements \citep{barnes1954,mitchell1969}. From employees embedded in organizational hierarchies to ties of friendship, kinship and trust, data describing complex networks is represented in the literature through so called \textit{graphs}. A graph is a mathematical object consisting of a set of nodes and a set of ties, each node representing the basic units of the system, and each tie representing a pair of connected units. To the naive observer, a graph is merely a model of the data, supposedly \footnote{The role of the model and the theoretical assumptions it conceals is both a matter of debate in the literature \citep{morgan1999,hacking1990,mitchell2009} and one of the critical points raised in the dissertation - see chapters 2, 6 and 7. } carrying only modest theoretical luggage \citep{morgan1999}. Adding theoretical propositions to the model paves the way for the classification, prediction and optimization of the system's behavior \citep{Wasserman1994,barrat2008,pastor2010,serrano2009,vespignani2009}. 

In contrast to the durability of social network, the association between individuals communicating via email, text messages or phone calls, is performed for a short length of time; the meaning and significance of these transactions often unknown to the observer \citep{holme2011}. Was an arbitrary email sent by mistake, or does it signify a meaningful connection between sender and recipient, traces in the data of a relationship relevant to the study at hand? Can one attribute any meaning to an email without knowing its context or even content? Moreover, how should one account for the various ways in which a tie is activated, and what consequences might temporal patterns of tie activation have? 

The first example for the importance of temporal patterns in networks concerns the issue of transitivity \citep{holme2011,rocha2010,moody2002}. Standard graph theory tells us that given a triplet, $ A $, $ B $ and $ C $, where one path exists between $ A $ and $ B $ and another between $ B $ and $ C $, there must also be a path between $ A $ and $ C $. Less formally, consider three actors, one of whom acts as a broker ($B$) between the other two (actors $ A $ and $C$.) The latter two actors can both exchange information directly with their broker. But even if those two actors cannot exchange information directly with each other, one might conclude that they can exchange information indirectly via their broker. Thus, networks have a property of transitivity.  However, consider what happens if $ (A,B) $ can exchange new information only \textit{\textbf{after}} actors $ (B,C) $ do so. If the temporal sequence of communication is consistent, then no new information can ever propagate from $ A $ to $ C $, violating transitivity under this regime of information transaction patterns. Without taking the temporal patterns into account, the study of diffusion of new information may yield faulty conclusions \citep{kempe2000,moody2002}. 

It is one thing to take a static network and to study its attributes; the types of nodes and edges, the role they play in the network and the properties of their subunits. But the research of networks often deals with questions about dynamic processes that unfold in the context of interconnected individuals. The classic example is the study of diffusion of viruses (or technology, information etc.) within a network of connected actors \citep{coleman1957}, where the existence of ties between individuals are conceptually separated from the discrete event by which one individual is infected by another. More generally, there is a distinction between the underlying static network and the set of events that the network facilitates or constrains. When is the temporal nature of transactions most important to take into consideration? When can it be ignored? \citet{holme2011} argue that the temporal nature of interactions should be taken into account when temporal structures are `not too random or too regular.' The authors warn that in those cases,  ignoring the temporal patterns of transactions could result in the loss of explanatory power, possibly leading to faulty results. 

To illustrate their point, \citet{holme2011} note the role of `bursty' patterns of human interactions in the propagation of sexually transmitted diseases. In a milestone publication in \textit{Nature}, \citet{barabasi2005} explains why certain types of human activity follow a random pattern, whereas others do not. Random events are typical of traffic flow and incoming calls in a call center, for example, each event occurring independently of past ones. In a call center, for example, the probability for a call within the next 5 minutes does not depend on whether or not the last call occurred just a minute ago or half an hour ago. In contrast to random events, `bursty' ones do exhibit interdependency over time. Consider the act of sending out emails. A relatively long period of inactivity could be followed by a short sessions in which users send out several emails in a quick succession. Sending emails is just one example of many types of task oriented and cognitive dependent activities that exhibit `bursty' regularities. Other examples include web browsing, library visitation, ratings of movies, mobile communications and trade transactions \citep{barabasi2010,song2010}. 

Understanding the temporal distribution of social activities is crucial for the study of how things spread in a collective, and specifically to the study of sexually transmitted diseases \citep{rocha2010}. In each sexual encounter between an infected and a non-infected individual, only a small dose of the pathogen is transmitted to the uninfected individual. In and of itself, this dose is usually not enough to infect the receiver and its concentration in the blood decays exponentially over time. Hence, a small number of sexual encounters, randomly distributed over a period of time, would allow the level of the pathogen in the blood to decrease enough between encounters, never reaching the critical threshold necessary for infection. In contrast, consider an uninfected individual practicing abstention over most of the same period of time, followed by a succession of sexual encounters. In this case, the concentration of the pathogen in the blood may not have enough time to decay, building up and much more likely to reach the critical threshold that results in infection. Thus, the propagation of the disease depends not only on the structure of the network or the number of sexual encounters within a given period, but also on the temporal distribution of these events \citep{rocha2010,holme2011}.

Another issue has to do with transactions that involve more than two actors at any one time. Networks represent connections between pairs of actors, since each tie connects exactly two actors. But social study becomes most interesting beyond two actors and just one single tie \citep{simmel1950}. More specifically, the study of interdependencies between one pair of actors and another pair is arguably at the very core of what it means to study networks. To address issues of tie-interdependencies, several methods have been developed in the literature, some of which present useful but ad-hoc techniques \citep{eckmann2004,kossinets2006,palla2007} whereas others \citep{snijders2006, brandes2009,butts2008} are more general but mathematically onerous and not always suitable for analysis, especially for large and complex datasets of social exchanges. This is unfortunate because in many cases interdependencies are explicit in the empirical data itself, they can be read off the data and need not be derived. For example when more than two individuals interact, the transactions between one pair could bears upon another pair. However, for reasons that are discussed in chapter 3, it is precisely this information about interdependency between distinct pairs that is discarded % \citep{engel2011}  
in the process of aggregating the data and encoding it in a way that is amenable to network analysis. 

Though the exchange of information can be done on a one-to-one basis, many such transactions involve a message that is broadcast to anyone that might listen. Studies of communication networks include spreading messages in blogs \citep{kumar2005,adar2005} and microblogs \citep{java2007,kwak2010}. Between one-to-one and broadcast messages, there is an intermediate form of transactions that occurs within bounded groups. Examples include the exchange of information in meetings \citep{gibson2005} or e-mails sent to multiple recipients \citep{zhou2005,engel2011,liben2008}. The process of handling the data in question, aggregating it and disaggregating it to obtain the final network model, has been shown to have substantial consequences on the network model and on the results of the research \citep{engel2011,quintane2011,krings2012}. As of today, there are no standard practices or accepted considerations, how to transform data into a network model, an open problem that presents urgent methodological and theoretical challenges. 

The examples above demonstrate some of the limitations and frontiers in the state of the art of social networks. They are especially onerous when studying communication networks \citep{monge2003}, when models are derived from digitally mediated communication exchanges, and when the evolution of the network is slow compared to the rate of transactions between individual nodes. This is less of a concern when the object of study is a static or slowly evolving network and more of a problem in situations where the network is derived from data consisting of event-type encounters, exchanges and transactions. Traditionally, researchers overcome this problem simply by aggregating the contact sequences. The implied assumption here is that a relationship is equivalent to a set of encounters. In some cases this is certainly an acceptable  approximation. But, depending on the objective of the research, this method may have serious limitations, as shown in chapters 3 and 4. 
 
Studying networks of ties and transactions is an interdisciplinary endeavor. For example, the study of sexual transmitted diseases involves not only the biological mechanisms by which infection occurs, but also the social, psychological and even political \citep{brandt1985} (!) processes that govern human sexual activity \citep{krieger1994}. The interdisciplinary nature of the problem isn't evident only within a study, but also across studies \citep{holme2011} and like many such problems, challenges and opportunities arise when researchers of different fields need to address similar problems. General issues include the development of an appropriate vocabulary and an adequate way to visualize the temporal nature of transactions associated networks, graphically demonstrating the relevant  statistics that are most suitable to characterize the system and to compare between them. Thus, where I use the term transactions, other authors use different concepts interchangeably that refer basically to the same thing, concepts such as `events' \citep{brandes2009}, or `relational events' \citep{butts2008}, `edge-activation' or `temporal networks,' \citet{holme2011} `functional networks,' \citep{shalizi2006} `interactions' and `social action' \citep{gibson2005} etc. A vocabulary that is not standardized can make it taxing to read related literature in and across fields. In addition, in order to benefit from insights in one field and adapt it to another, the reader needs to attain some degree of proficiency in an otherwise unrelated field. And yet social scientists, physicists, epidemiologists, neuro-scientists and others need to learn from one another in order to enrich the understanding of their own field. 

\section{Dissertation outline}
Against the backdrop of the problem presented above, this work is guided by a research question that addresses the \textit{\textbf{links between social transactions (at the micro level) and networks of social ties (at the macro- and meso-level) }}. 

\figuremacroW{ImgSocialHysteresis}{Social Hysteresis}{micro-level interactions and macro-level networks and ties}{.7}

Now, before narrowing down the question and qualifying it a bit, I would like to illuminate it by presenting an example of a possible answer, the idea of a social hysteresis suggested by \citet{elster1976} and depicted in figure \ref{ImgSocialHysteresis}. It is best to explain the idea by recalling again the two lovers from Ibn Hazm's poem. They meet each other for the first time at the graph's origin and start exchanging their bashful gazes, slowly climbing along the lower path of the hysteresis curve. At first, the path is delightfully convex, each of their interactions carries very little baggage in terms of commitment and mutual obligations, the bond between them builds much more slowly than the frequency and emotional intensity of their interactions. But an unfortunate turn of events changes all that - maybe they have been spotted by someone else, friends or relatives, and the path curves upwards, becoming uncannily steep; each small interaction carrying with it more expectations as the prospect of marriage and an institutionalized relationship looms near. After the wedding, the pair might turn and fall back to the origin, this time taking the upper path of the hysteresis curve:  micro-level interactions decrease in emotional intensity and slow down in frequency, at first without apparent damage in commitment, trust and mutual respect, later with palpable effect on the relationship. 

Unfortunately, this dissertation will not provide such a neat answer to the question, primarily because it is very hard to measure the social tie in a direct way. It is possible to show empirically that ties do have explanatory power (see chapter 5), but it is difficult to learn a great deal about their properties in detail. In addition, the dissertation focuses not just on a pair of lovers but on a network of members in an organization, engaging in email transaction, emails being just one of of several modes of communication. 
% Now, to supply such a neat answer to the question one would need two independent variables - one measuring the intensity and frequency of interactions over time, and one measuring the depth of the commitment and the strength of the tie. Unfortunately, access to the latter variable is usually rather costly and 
By focusing on networks in organizations and the technology that mediates transactions between individuals, the dissertation strives to contribute to the literature of organization studies and information systems. On the one hand, organization studies promotes the understanding of structures and processes within (and between) organizations, addressing, among others,  problems of coordination and control, hierarchy, power, boundaries, performance and evolution of organizations. In so doing, it departs from traditional neo-classical economic approaches that treat firms as profit-maximizing black-boxes, optimally adapting to fit their (exogenously) changing environment \citep{coase1937}. 

Somewhat related to organization studies, the study of information systems strives to understand the role and consequences of information and communication technologies (ICT) in communities, organizations or other social contexts. It addresses problems of usability, adoption and unintended consequences of ICT, creating a corpus of knowledge that focuses on `the development of IT-based services, the management of IT resources, and the use, impact, and economics of IT with managerial, organizational, and societal implication' \citep{about_mis}. In so doing, it departs from traditional approaches that treat technology as a black-box deployed to increase the efficiency of carrying out tasks. 

Within this disciplinary framework, the dissertation examines the mutual influences between networks of ties and email transactions exchanged between its members, albeit somewhat obliquely \citep{kay2012}. Because of its circular nature (transactions affecting network and being affected by them) a direct approach could follow the footsteps of \citet{manski1993,manski1995}, in his investigation of so called `endogenous' or `correlation effects' . But this dissertation adopts a less direct, less theoretic and more concrete approach; instead of testing the mutual influences between ties and transactions directly, the dissertation proceeds by focusing on a practical issue that faces many researchers: given a dataset of email transactions, what are the alternative options to construct a network model? By investigating this question, exploring different avenues and comparing them, the intention is twofold; first, to tackle a pressing practical issue and second, to allow theoretical issues to arise in the process and to focus on each as it emerges. This is an exploratory approach, to some extent data-driven, making use of two empirical resources in the form of email datasets; one of which is a snapshot taken from the famous Enron corpus \citep{shetty2004} and the other is a dataset of email communication taken from a European university\citep{eckmann2004}. 



% \citep{Shetty2004,diesner2005}. This corpus was released by Federal Energy Regulatory Commission (FERC) after the collapse of the Enron corporation, an American energy, commodities, and services company. It consists of a database of actual emails from 158 employees. The unprecedented access to this raw data was motivated by FERC's wish to increase the transparency of the investigation and to improve the public understanding of the various reasons that led to the collapse of the firm. The dataset attracted the attention of researchers in social networks, organization theory and management. It is more detailed than any other popular email datasets, it is widely studied, easily accessible and therefore a convenient source of data \citep{murshed2007}. 

%The second dataset was extracted from the log files of one of the main mail servers at a university \citep{eckmann2004}. It consists of a record for each e-mail messages sent during a period of two months and includes about 10,000 users. The data is less detailed than the Enron corpus; it does not include the contents of the messages and many other details. The limitations of the second dataset meant that it was impossible to replicate the exact same analysis on both data sources. However, this was enough in order to establish several common findings that are perhaps less sensitive to changing contexts, as well as findings that vary between the datasets, exhibiting perhaps context dependent features. 


%Micro-foundations has become an important theme in studies of organizations, in the past decade or so. Its basic premise is that an adequate explanation of collective phenomena needs to involve explanatory mechanisms (psychologies and situations) of the participants in those phenomena (Miller, 1978; Udehn, 2001, 2002)." 

The theoretical and the methodological approach chosen has different names and various interpretations \citep{udehn2002}, but in what follows I use the term micro-foundations. Micro-foundations is becoming an increasingly important theme in studies of organizations in the past decade or so. Its basic premise is that an adequate explanation of collective phenomena needs to involve explanatory mechanisms (psychologies and situations) of the participants in those phenomena \citep{miller1978,udehn2001,udehn2002}. Scholars are paying substantial attention to explanatory mechanisms that involve individual incentives, preferences, opportunities and action in understanding issues such as financial performance \citep{abell2008},  resource value \citep{lippman2003b, foss2005}, value appropriation \citep{lippman2003a, coff1999, barney2001},  inertia \citep{kaplan2005}, strategy implementation \citep{barney2001}, firm-level heterogeneity \citep{gavetti2005}, factor market dynamics \citep{makadok2001} and other properties of organizations\footnote{For a review of micro-foundations based studies of organizations, see \citep{felin2006,foss2010}}. In their search for the micro-foundations of collective phenomena, researchers of organizations follow the steps of a similar research projects in macro-economics \citep{leijonhufvud1967,colander1993,carlin2006} and rational choice sociology \citep{elster1989,elster1998,coleman1990,abell2003}. 

Having touched upon the research question and the theoretical and methodological issues at stake, I turn now to outline the next chapters.


\subsection{Chapter 2 - Strangers but Bedfellows: Transactions and Ties}
The review of the theoretical literature opens with two puzzling observations: first - despite the exploding availability of cheap and large-scale electronic datasets describing human transactions \citep{watts2004,watts2007,lazer2009}, network researchers continue to rely primarily on traditional questionnaire-based data collection methods to test and advance substantive network theory\citep{quintane2011,marsden2005,marsden1990}. 
Moreover, comparative studies report systematic differences between social network data extracted from observed interactions and those extracted from survey-data: people report one set of social ties, but their interactions suggest a different set of associations \citep{krackhardt1987,killworth1980,bernard1980,bernard1984,bernard1990}. 

There are several issues here at stake, one is a methodological problem (imperfect recall and report bias for example.) The second is the conceptual difference between ties and transactions. This chapter focuses on this conceptual difference that makes reported relationships more amenable to the traditional study of social networks. This does not mean that digital transaction data do not have the potential to inform and contribute to theoretical developments in the field \citep{lazer2009,wimmer2010,szell2010}, but it suggests that there may be a need for conceptual developments if the datasets are to be used to inform the study of networks. 

This conceptual issue is then anchored in a long standing controversy in the social sciences, epitomized in the momentous debate concerning the nature of sociology and its relation to other sciences, a debate that took place between Gabriel Tarde and \'{E}mile Durkheim at the  \'{E}cole des Hautes \'{E}tudes Sociales in 1903 \citep{vargas2008,Karsenti2010}. Attempts to synthesize between Tarde's nominalism and Durkheim's realism can be found in the works of Marcel Mauss, Georg Simmel and Max Weber, all of whom contribute to the development of definitions used in the dissertation: social (trans)actions and social ties, networks, structure and tie-interdependency. 

The second part of the chapter reviews the theoretical literature that concerns the micro-foundations approach. This approach is based on a fundamental distinction between macro-level properties and micro-level ones, the first are attributed to the system at large; network connectivity,  social norms, an organization's hierarchical structure, routines and capabilities are examples thereof. The transactions between individuals, in the form of discrete events of interaction and communication, are located at the micro-level. In addition to these two levels, a third one, the meso-level is considered, in the form of the ties that connect individuals. Hence, in theoretical terms, the focus is on the mutual influences between networks (macro-level), nodes and ties (meso-level) and transactions (micro-level): on the one hand, nodes and ties are seen as the context against which sequences of transactions unfold. On the other hand, transactions are considered to have (cumulative) consequences on the maintenance and evolution of social ties. 

The third part of the chapter demonstrates how these issues pertain to the study of social networks, examining the history of this field with special attention to the issue  of transactions and ties and arguing that its philosophy is more closely related to Durkheim's epistemological realism. This comes at the price of neglecting, to some extent, more process driven phenomena. Crucially, the longitudinal study of evolving networks is a rich and highly sophisticated endeavor \citep{snijders2005}, but besides the issue of diffusion, the most exciting events occurring in networks are tie creation and deletion, a product of endogenous effects contingent on the configuration of existing ties at the local environment. Three main effects explain how ties turn on or off; the popularity effect \citep{snijders2006}, homophily \citep{McPherson2001,robins2001,robins2001a}, and triadic effects \citep{snijders2006,feld1982}. All of these mechanisms are examples of tie interdependencies, but also examples of a micro-foundational mechanisms in which the act of tie creation is made at the local level, giving rise to macro patterns at the level of the network as a whole. 

However, from the point of view of traditional network research, the lowest level in which anything happens is still the level of the tie\footnote{But see \citet{brandes2009} for some exceptions}. But creating or dissolving ties is altogether a different type of event to that of sending out emails or engaging in a transaction. The difference is in the level of abstraction and aggregation -- conceptually, a social tie is located at a higher level of aggregation than a social transaction, since every tie is associated with multiple transactions. And though the literature of social networks has devoted much attention to the first type of event, there is relatively little research within the field regarding the second type of event. 

The identification of this gap in the literature of social networks paves the way to a systematic comparison between transactions and ties, along a couple of dimensions such as lifetime cycle, the type of variables, the hierarchical structure of the data, the network density and methods of data collection. At first sight it seems that the relationship between transactions and ties is that of supervenience: every tie is associated with numerous transactions, and every (one-to-one) transaction is associated with exactly one tie. But upon closer inspection we find that this works only as an approximation, and that the data reveals a crossed, non-hierarchical relationship. Every node is involved in more than one tie and every tie involves more than one node (exactly two nodes, to be precise.) In addition, ties and nodes do not really supervene on transactions when dealing with a one-to-many email exchange: when sent to more than one recipient, emails span not only one, but multiple ties. This is good news for those who are interested in studying interdependencies between ties, bad news for those who work with network models that are ill equipped to take this kind of interdependency into consideration. Network scientists, interested in tie interdependencies thus face a dilemma.

The chapter ends with a research question that will guide the rest of the dissertation, a question concerning the relationship between social ties and transactions in organizations, where the transactions are in the form of email exchanges. The technological medium by which transactions are exchanged is highlighted, just as the organizational roles and the properties of the individuals in the organizations must be taken into account. A discussion about the importance of technologically mediated transactions brings the chapter to a close, highlighting the potential of such data to inform the development of `new frontiers' \citep{parkhe2006} in network theory.  

 
\subsection{Chapter 3 - Forging Networks from their micro-foundations}
The methodological chapter spells out the methods used to address the research question described in the preceding chapter. The first issue is how to transform digital communication datasets into network models. Three different approaches used in the literature are reviewed, and their merit evaluated for the purpose of this dissertation. The problem is a common one in the empirical research of communication networks: how to construct network models from raw data. Email communication datasets are typically transformed into networks in which nodes designate email users and ties connect nodes if an email has been dispatched from one of them to the other. Each email is treated as a star shaped `ego-network' \citep{freeman1982} with the sender in the center of the star, connected by an edge to each of its recipients. The ego-networks are then interlaced and superimposed on top of one another, creating the entire network. Unfortunately, this method of extracting sender-recipient pairs from multiple recipient emails discard important information regarding the nature of affiliations and the process by which they come about
% \citep{engel2011}
. Specifically, recipient lists of a user's outgoing emails delineate meaningful organizational units so that being co-recipients on a single email is a stronger indication of affiliation than being recipients of separate emails from the same user. Whereas the first may designate a part of a collaborative effort (such as a group discussion,) the second suggests a series of independent transactions.  

The following chapters use empirical datasets of email communication, constructing networks that account for information that is usually discarded, testing the merit of the network models against models that do not take this information into account. The empirical data consist of two datasets, one of which is a snapshot taken from the famous Enron corpus \citep{shetty2004,diesner2005}. This corpus was released by Federal Energy Regulatory Commission (FERC) after the collapse of the Enron corporation, an American energy, commodities, and services company. It consists of a database of actual emails from 158 employees. The unprecedented access to this raw data was motivated by FERC's wish to increase the transparency of the investigation and to improve the public understanding of the various reasons that led to the collapse of the firm. The dataset attracted the attention of researchers in social networks, organization theory and management. It is more detailed than any other popular email datasets, it is widely studied, easily accessible and therefore a convenient source of data \citep{murshed2007}. 

The second dataset was extracted from the log files of one of the main mail servers at a university \citep{eckmann2004}. It consists of a record for each e-mail message sent during a period of two months and includes about 10,000 users. The data is less detailed than the Enron corpus; it does not include the contents of the messages and many other details. The limitations of the second dataset makes it impossible to replicate the exact same analysis on both data sources. However, it is enough in order to establish several common findings that are perhaps less sensitive to changing contexts, as well as findings that vary between the datasets, exhibiting perhaps context dependent features.

The core of the chapter discusses three different methods, by which networks can account for phenomena that are located at the micro level of transactions. The first method uses tie-strength to reflect the temporal patterns of transaction. The idea is that every email between two users reflects and re-affirms the tie between them, so that the more emails are sent between two users, the more meaningful is their tie. Moreover, an email with only one recipient may signify a `stronger' relationship between the sender and recipient, whereas an email sent to numerous recipients signifies a `weaker' relationship between the sender and each of its recipients \citep{newman2005}. Another approach to the use of tie strength is to consider a time dependent function that describes the strength of the tie as it changes with time \citep{palla2007}. The key advantages for using the strength of ties is that this method is relatively straightforward, both conceptually and mathematically. It uses more of the data than traditional methods, enriching the network model and representing more of the original network data in a way that is compatible with existing methods of network analysis. Accounting for non-binary edge values is a natural extension of the existing toolkit of network analysis; centrality and density for example, group search algorithms and clustering indicators have been extended in the literature to account for tie-strength \citep{opsahl2009}. In chapter 4, an attempt is made to validate this method to show that using the idiosyncrasies of email communication to calculate tie-strength yields a result that is more useful from a social science perspective. The downside of this method is that much of the important information is still discarded, especially with regards to the issue of tie interdependencies.

The second method uses an unusual type of social network that contains not one, but two different types of nodes, one type reflecting individuals and the other reflecting groups associated with the individuals. Known as `two-mode networks' or `bipartite-graphs', these models have been used to represent various systems \citep{latapy2008}. Examples include systems of interlocking directors  \citep{robins2004}, in which each company is associated with multiple directors and each director associated with multiple companies \citep{latapy2008},  or actors-films network, where each actor is linked to each one of the films she played in \citep{Watts1998}. Using a bipartite graph, the email dataset is transformed to a network in which individual email users are represented by one type of node and their emails represented by a different type of node. Theoretically, this method conforms to the work done by \citet{Feld1981}, a theoretical call to extend the theoretical toolkit of networks with the idea of an `organizational foci,' social or psychological `entities around which joint activities are organized' \citet{Feld1981}. Whether emails can be seen as an organizational focus is a matter of some debate, but the advantage of this method is that it represents the original dataset with much greater fidelity than the regular, one-mode network. The drawback is that the resulting bipartite network has some unusual properties, and that the literature on two-mode networks is not quite as developed as the single-mode network: there are still many open questions, both theoretical and methodological, regarding basic measures in bipartite networks \citep{opsahl2011}. Both mathematical and conceptual challenges make this method particularly onerous, though future work may take up this idea and develop it further. 


% Opsahl2011 (?) Many large real-world networks of interest may be modeled naturally by a bipartite graph. These networks are called two-mode networks, or affiliation networks when they represent groups and members (i.e., each link represents a social actor’s affiliation to a group). Let us cite for instance the actors–movies network, where each actor is linked to the movies he/she played in (e.g., Watts and Strogatz, 1998 and Newman et al., 2001a), authoring networks, where the authors are linked to the paper they signed (e.g., Newman, 2001a and Newman, 2001b), occurrence networks, where the words occurring in a book are linked to the sentences of the book they appear in (e.g., Ferrer and Solé, 2001), company board networks, where the board members are linked to the companies they lead (e.g., Robins and Alexander, 2004, Conyon and Muldoon, 2004 and Battiston and Catanzaro, 2004), and peer-to-peer exchange networks in which peers are linked to the data they provide/search (e.g., Fessant et al., 2004, Voulgaris et al., 2004, Guillaume et al., 2005 and Guillaume et al., 2004).


Whereas the first two methods are applied empirically in chapter 4, chapter 5 develops a method that resonates more with the theoretical framework of micro-foundations. This method is based on multi-level analysis \citep{snijders1999,snijders2003,courgeau2003}  and applies them to the two different datasets of email communications. The method is especially interesting from a theoretical point of view, because it views a transaction as a potential product of properties at different levels of aggregation. A given transaction can be triggered by preceding transactions (at the micro-level), it is affected by the individuals involved (sender and receiver(s)), and by the history of their relationships (at the meso-level.) Finally, it is affected by the macro-level properties of the system. Teasing out the sources of variance among the different levels of aggregation is important theoretically, but it has different applications as well. For example, the method can be used to compare within each of these factors, e.g., comparing email users to one another in terms of the way they use emails. The method can also be used to compare between the factors. This could be important for those interested in marketing and customer relations, for example. Say a customer reacts to a certain message sent by the marketing department. It would be of interest to learn the reason for this `success,' why did the message elicit a response? How much importance should be attributed to the sender's properties (`sender effect')? Is her role or reputation such, that her mails are rarely left unanswered? How much should be attributed to the overall responsiveness of the customer (`recipient effect')? What is the relative importance of the specific tie between the sender and her customer (`tie effect')? What is the relative importance of the message to elicit a reply (`message effect')? 

Multilevel analysis is a method that offers ways to think about these questions. Its downside is that it is not adequate for large datasets and that triadic effects are not really taken into account. In fact, the data is not treated as a full fledged network but as a hierarchy, that consists of one-to-one transactions, ties and individuals. Whether it is possible to extend the model in future work to account for triadic effects is an open question. The methodological section ends with a discussion of how extant literature on email communication technology informs the different methods discussed and how the development of these methods can contribute to the extant literature in information systems and organizations research. 
 

\subsection{Chapter 4 - Exploring Email Networks}
The first empirical chapter explores the distinct character of email datasets, setting  it apart from traditional survey based network datasets. One of the first most distinctive features of social networks, is the puzzling distribution of the number of ties associated with each individual, also known as the `degree' distribution. In contrast to random networks whose degree distribution is normal (i.e., they have a `thin-tail',) social mechanisms that govern human behavior create `fat-tailed' distributions that follow a power law. The non-random nature of people's relations has prompted researchers to study the social mechanism that governs this (macro)-phenomena and in a landmark publication in Science, \citet{barabasi1999} showed that the `popularity' mechanism provides a sufficient explanation for this empirical finding. 

But unlike social network, in email datasets people are not connected to others \textbf{\textit{directly}}. Their connections are mediated through their email transactions. Thus, instead of simply having one measure of connectedness, namely the number of ties per individual, we now have three; `email production' is the number of emails produced (and sent) by individuals, `email consumption' is the number of emails received by individuals, and `email dissemination' is the number of recipients on each email. The chapter shows that all three of these exhibit non-random, heavy tailed distributions, a finding that demands to extend the `popularity' mechanism to patterns of email usage.

The third part of the chapter draws on the first two methods introduced in chapter 3, two different ways to construct network models from email datasets while at the same time trying to gain more fidelity with the original dataset as compared to traditional methods. First, I attempt to develop a bipartite graph that represents users and email objects themselves, and very quickly I show how this method becomes too complicated and does not conform with the standard toolkit of social networks. Finally, I introduce the notion of tie-strength in order to represent relevant information in the dataset. I then validate this method, showing that it yields better network models than the alternative.

The last part of the chapter shows the importance of the medium of transaction, email messages in this case, to reveal different structural patterns in the data. The intuition here is that an email sent to a single recipient has a different meaning, function and consequences than sending an email to multiple recipients. To test this proposition, several different networks were constructed using the same group of 71 users over the same period of time, but each network was based on emails with a different number of recipients. Comparing networks derived from single-recipient emails (private transactions) with networks derived from multiple recipient emails (broadcast transactions) reveal systematic differences. First, reciprocity is greater in private transactions than in broadcast transactions. Second, broadcast transactions reveal a structure that is more clustered and tightly knit, whereas private transactions reveal a structure that is less clustered and more hierarchically structured. These findings open theoretical questions as to the social mechanisms that explain them, an issue that is addressed in the analysis and discussion chapter (chapter 6.) A second insight is that the way people use the same technology matters, that different uses of technology reveals various networks, suggesting that within a community there exists not one, but multiple `layers of structure' existing side by side. A third insight is that the traditional method of constructing social networks from email dataset is problematic, in the sense that it discards important information regarding interdependencies among ties.

The aim of the chapter was to construct network models that reflect the empirical dataset of email transactions. The findings indicate that the resulting network model is sensitive to decision made in the process of constructing networks. Moreover, critically evaluating the process of network construction reveals interesting patterns that demand explanation: the curious distribution of email production, consumption and dissemination being one example. `Layers of structures' being another. Chapter 6 attempts to suggest explanations to these findings in the form of mechanisms that operate at the micro-level. But before using theory to make more sense of the data, let us turn to a third method for analyzing the data empirically. 


\subsection{Chapter 5 - Four Factors Influencing Email Effectiveness }

The second empirical chapter applies the third method introduced in the methodological chapter to both datasets. The intuition that guides this method is that an email transaction is not necessarily a one-off event, but is embedded in chains of related transactions, not unlike the `chains of interactions' idea developed by Erwin Goffmann and his students \citep{collins2004}. According to this view, each transaction is potentially a stimulus for the next email in the chain, as well as a possible response to a previous transaction. Thus, each email is one transaction that must be understood in the context of the chain of transactions, emails bouncing back and forth between the users in the network, people collaborating or discussing a problem and together reaching a form of consensus, thereby re-affirming or reconfiguring the nature of their ties. 

Transaction sequences and social networks are conceptually distinct and not easily reconciled, two different programs of research, a division of labour within the social sciences themselves between `interactionists' and researchers of `networks,' two different camps sharing a sense of mutual suspicion \citep{gibson2005}. This chapter attempts to follow the advice of \citet{gibson2005,gibson2008}, searching for the intersections between these two sub-disciplines. Thus, an email is not only an indication of a network tie, but also as a discrete bead in a stimulus-response chain of interaction. 

For the purpose of this study, an email is considered `effective' to the extent that it succeeds to elicit a reply from a recipient. For every email sent from $A$ to $B$, a reply email is searched for, one that was sent from $B$ to $A$ at a later time and shares the exact same subject (ignoring subject prefixes such as `fwd' or `re' that are added automatically by the email client software.) For each email from $A$ to $B$, the binary outcome variable measures whether the recipient $B$ has replied. Four effects are estimated. The sender effect measures to what extent emails from a specific sender, $A$, are likely to receive responses. Some senders are found to be much more (or less) effective in eliciting replies than other senders, independent of who the recipients are. The recipient effect measures the responsiveness of each recipient to emails. Some of recipients are found to be much more (or less) effective in replying to emails than others. The tie effect measures to what extent a specific tie between a pair of users is responsible for a high level of responsiveness. It is possible that two users are much more (or less) responsive to each other than they are to the ties they have with other people. The fourth effect is the effect of the message itself -- perhaps there is something in the message itself that is responsible for a high level of responsiveness from its recipients. 

The findings show that all these four effects are important, to a varying degree. Besides the importance of teasing apart effects at different levels of aggregation, the model validates the findings in the previous chapter, showing that the number of recipients is significant and inversely proportional to the probability of eliciting a reply. Second, there are several practical explorations that ensue from these findings. For example, one dataset shows that sender effects and recipient effects of the same individuals are positively correlated, while in the other dataset they are negatively correlated. To understand exactly why we get different findings in different networks, we would require access to more data about the organizations, the individuals working in it and their organizational roles. But for our purposes it is enough to note that some findings are consistent between networks and other findings vary. In this way effects at all three levels of aggregation were identified, each playing their role in determining human action: effects at the micro-level of past email transaction, effects at the meso-level of the ties and nodes, and finally effects at the level of the network itself. 

%The two perspectives are not easily reconciled. Predictably, what one sees as fundamental, the other views as lacking in substance. Speaking from an interactionist perspective, Schegloff (1987b), for instance, calls  into doubt the theoretical status of mesolevel constructs, of which “network”  is one, for their lack of demonstrable relevance to participants at specific conversational junctures. Network analysts, on the other hand, implicitly view the details of interaction as so much airy chaff, posing little resistance to network effects which, given enough time, will carry the day. Methodological differences widen the divide further. Network analysis is primarily quantitative (Wasserman and Faust 1994), which is necessary both for statistical analysis of network structure taken as an object of study unto itself (e.g., Lazega and Pattison 1999; Watts 1999), and for discerning network effects on outcomes such as job promotions (Burt 1992; Podolny and Baron 1997) and contagion (Burt 1987) where other independent variables are in play. In contrast, conversation analysis, the interactionist school of most interest here—given its emphasis on the direct study of sequences—is mainly qualitative (Schegloff 1993), as it needs to maintain maximum openness to the myriad ways in which a given utterance can be precipitated, warranted, or otherwise occasioned by the talk preceding it (Schegloff 1987a).There have been a few bridging attempts.

% Email communication consists of chains of related social transactions. Each email may serve as an invitation or stimulus for the next email in the chain, possibly setting in motion a series of related emails that bounce back and forth between actors. At least for a certain period of time, this process may activate social ties and realize network 10 congurations. This paper probes the mechanisms underlying this process, what could be seen as a `collective action' of sorts. The basic building block of such a process is the email that acts as a `stimulus' for subsequent and related emails. Whether such a `stimulus' is eective in marshaling a reply depends on various factors. Four factors are considered in this paper; (1) proper- 15 ties of the email's sender (sender eect), (2) properties of its recipients (recipient eect), (3) properties unique to each sender-recipient dyad (dyad eect) and (4) properties of the email that may or may not trigger a reply (stimulus eect).



. 
% reciprocity, mutuality and exchange
% four factors SMCR account in byron2008
% Add stuff like agreement in the attributes (homophily), of the recipients/senders, the degree of the recipient/receivers etc (like in de Nooy and Iribarrena and Moro). 
% Newspapers slant is a demand side effect, not a supply side effect http://www.nber.org/papers/w12707
% The four factors...
% Licoppe - different medium/channels for different messages Markus1994 - employees have a stated preference for email when delivering negative information



%


%To test the merit of the models, an outcome variable at the micro-level of user's action is defined. For example - given an email sent to a recipient, will the recipient respond, yes or no? The variability of this outcome is partitioned into four factors representing four sources of variability: the sender, the recipient, the dyad, and the multi-recipient email. This method separates the `reasons' for action between meso-level and micro-level contexts. It takes into account not only properties at the meso-level of the sender and the recipient, but also the properties of the history of the relationship they have. At the micro-level, the model takes into account the  

\subsection{Chapter 6 - Antaeus and Micro-Foundations}
After presenting the findings, this chapter turns back to the theoretical framework discussed in chapter 2, attempting to explain the empirical results in light of its micro-foundations. The chapter begins by showing how other empirical studies used the micro-foundational analysis to make sense of their findings. The typical approach in the literature is first, to distinguish between macro-properties of the system and micro-properties that are located at the level of individuals. The second step is to assume that micro-level entities abide by a few simple rules of interaction in order to observe whether the dynamics of interaction lead to  properties observed at the macro-level of aggregation. This approach is most general, and does not depend on whether the study involves atoms, gas, molecules, swarms, markets, etc. \citep{moussaid2009,minsky1988}. One of the critiques of this method is precisely the fact that it too general, raising the question whether or not the social sciences are different in kind from the rest of the scientific disciplines \citep{latour2012}. But a closer inspection suggests that there are in fact important elements that distinguish social mechanisms from other natural ones, such as the notion of expected utility, purpose and interests of individual actors. 

Over fifty years ago, Siegfried F. Nadel described \citep{nadel1957} what he found most interesting in the concept of `networks' with the following visionary words: `...I do not merely wish to indicate the 'links' between persons; this is adequately done by the word relationship. Rather, I wish to indicate the further linkage of the links themselves and the important consequence that, what happens so-to-speak between one pair of [nodes], must affect what happens between other, adjacent ones.' What Nadel was looking for was of mechanisms that govern tie-interdependency. Three mechanisms of tie-interdependency are referred to very frequently in the literature of social networks, namely popularity effects, homophily and triadic closure \citep{snijders2006}. Despite the common reference to these effects in the literature, many are still worried with the `inadequacy' \citep{snijders2006,newman2003} of these mechanisms, partly because they are they are still not well understood in terms of the situational processes that give rise to, and sustain them. It is infrequent that people `decide' to establish new relationships or dissolve old ones, since changes in the status of ties are themselves the result of a culmination of a process involving various situations, decisions, emotions, circumstances and mutual social transactions \citep{Lazarsfeld1954,hartup1997}. The literature on friendship makes  \citep{hartup1997} an important distinction between deep-structure (based on reciprocity)  and surface structure (social exchange), and the mutual influences between these two levels is precisely the theoretical motivation that guides the rest of the chapter. 

The second part of the chapter discusses the findings of chapters 4 and 5 in detail and proposes alternative mechanisms that are located at the micro-level. The first finding is the differential contribution of emails to reciprocity and clustering at the level of the network, depending on the number of its recipients. Three main mechanisms are discussed: the strength of weak ties, social loafing and signaling theory. Which mechanism is dominant depends on several factors, and the project of adjudicating between the different mechanisms merits future research, one that will controls for all but one mechanism to test its relative importance compared to the others. 
 
With regards to the question of tie-interdependency, multiple recipient emails are a powerful example of how ties are instantiated or reinforced in tandem. Recipients become aware of the existence of other co-recipients on emails. This has consequences on actors’ beliefs and the way they judge the expectations of email sender and the other co-recipients, ultimately effecting subsequent transactions. This approach prompts us to think of emails not only in terms of a tool used by scientists to uncover underlying organizational structures, but also as a set of related situations which themselves perform the network and steer its unfolding structures. 

The third part of the chapter develops the theoretical part of the micro-foundations approach further and demonstrates that its application in this situation is not at all obvious, since the relationship between objects is rarely one-to-many but mostly many-to-many. One of the conceptual problems is whether or not the macro-level entities exist sui-generis, or whether they are completely reducible to the micro-level. These questions are related to the realism vs. nominalism debate introduced in chapter 2. Posing the questions here in the concrete terms of emails, individuals and ties, epistemological realism seems to resonate more with the empirical findings, but this could be partly because the statistical aparatus is founded on epistemological realism and hence, arguably, having the results framed in this manner should not come as a surprise. Some of the fine details of the theory are discussed, as well as the critique found in the literature, especially the concern that the ideas of the micro-foundational approach are either implausible or trivially true \citep{miller1978,latour2012}. 

 \subsection{Chapter 7 - The rebirth of `social physics' }
This chapter summarizes and concludes the dissertation. Summarizing the main ideas, we are reminded that the departure point of this dissertation starts off with a theoretical observation and a methodological one. Theoretically, a distinction is made between social ties and social transactions. Methodologically it acknowledges the the gap between the structure of the data and network model, a gap that is overcome by a process in which transaction datasets are transformed into network models.  

By evaluating the different alternatives and validating them, we stumbled on surprising patterns that need further exploration and investigation, new types of phenomena that are contingent to the type of technology that mediates the transaction - the email. Key findings are then analyzed in light of the theoretical framework of micro-foundations explained in the second chapter, and implications and limitations of the theory and methods are discussed. This section concludes with some of the limitations of the methods, theory and findings in the dissertation, and some suggestions as to how to advance the topics raised in the dissertation.

The second part of the chapter is a short contemplation on the impact of new types of communication data, its sheer volume changing not only the scientific field of social networks but perhaps also the way we think about the `society', the metaphors we use to describe the social world and the lens we use to interpret it. The term `network' is entering the popular culture to such an extent, that political players use it to communicate their understanding of the world and the way one should act upon it. As an example, consider this extract from an essay written by Julian Assange \citeyearpar{assange2006}, founder of Wikileaks in 2006: 
\begin{quotes}
We will use connected graphs as way to harness the spatial reasoning ability of the brain to think in a new way about political relationships. These graphs are easy to visualize. First take some nails (“conspirators”) and hammer them into a board at random. Then take twine (“communication”) and loop it from nail to nail without breaking. Call the twine connecting two nails a link. Unbroken twine means it is possible to travel from any nail to any other nail via twine and intermediary nails. Mathematicians say the this type of graph is connected. 
\end{quotes}
% read more about the Deleuzian interpretation of this quote here http://fixingtheeconomists.wordpress.com/2010/12/19/the-deleuzian-philosophy-of-julian-assange/
Without actually judging Assange's politics or style, the use of the network metaphore in this context, as well as the ideas that it is supposed convey, suggest perhaps that we are entering a new chapter in the history of ideas. Might recent developments in ICT have as significant a scholarly, managerial and political impact in the twenty-first century as did the birth of Statistics on structuralism and its associated ways of thinking and acting in the twentieth century? 

This question merits a whole dissertation in and of itself, but some of the ideas in this dissertation seem to resonate with the changing landscape of the science of social networks. Three issues are discussed in detail: first, the entrance and impact of non-sociologists into the field of social networks. Physicists, computer scientists and mathematicians are publishing highly referenced papers on social networks in high impact outlets such as \textit{Science}, \textit{Nature} and in physics journals. The methods they use are mostly data driven, rather than theory driven. At their disposable are unprecedented amounts of transaction data at the micro-level, but very little data about social ties in the sociological sense of the word. What does that mean for the field as a whole? How is the landscape of social networks changing? How might we visualize the challenges and the opportunities that lie ahead? These questions are discussed frequently within the community of social network scientists, and they are the questions guide this chapter. 

% lit review of the papers that talk about social networks and the hopes for the future
% from Dauphine some of the insights
% data collection 


%Two basic research questions: 
%1. What are the implications of contemporary communication/interaction datasets on the research of social networks. 

%2. How to reconcile the differences between networks of ties  and networks of transactions?  / Mutual influences between networks of ties and networks of transactions. 

%Challenges: 
%1. studying networks today is like aiming at moving target: more and more ways of gaining access, technical development, brain sciences, neural networks - they are all changing the way we understand our society, the metaphors we use etc... By the time you try to fit new research and mode of thinking into an overarching framework, five new revealing studies have been published ... using google alert, defining who is doing the interesting bits, especially kovanen, saramäki, onnella, snijders etc...

%--> however, many of the ideas and questions are roughly the same, the relationship between the individual and the social, between the natural and the artificial, between the mediator and the mediated etc...

% \section{Theoretical and Methodological contributions}

% the literature review
% research questions... 
% theoretical background, 
% the literature review





\section{Conclusion}

This chapter started off by observing light-weight social transactions morph and crystallize into robust and heavy-weight structural ties, highlighting the tensions and interdependency between the two concepts. The links between durable ties and the flow within them is a matter of concern not only for social sciences, and thus it serves as an interesting meeting-point for various disciplines. The diversity of phenomena involved does not invite the development of a grand-theory to address them all, but the work of a bricoleur: developing ad-hoc tools, being sensitive to each new puzzle as it emerges, solving one at a time. 

In one of his most popular essays, Isaiah Berlin \citeyearpar{berlin2011} recalls an old expression made by the ancient Greek poet Archilochus: `the fox knows many things, but the hedgehog knows one big thing.' Berlin uses this to distinguish between two types of intellectuals.  Hedgehog intellectuals specialize and zoom-in on one problem, following one big idea during their entire career (Berlin's examples include Plato, Dante, Pascal, Hegel, Dostoevsky, Nietzsche.) Fox-like thinkers draw on various experiences, mix and match, diversify and adopt without settling down on a particular, single idea (Berlin's examples include Aristotle, Shakespeare, Montaigne, Goethe, Pushkin.) This dissertation took the vantage point of the fox, and in the effort to find new ways to construct network models, it embraced several surprising findings as it went along, each leading to new paths and challenges. 



%Two more points derived from the interdisciplanary nature of studying ties and transactions bricolour/hedgehog vs. fox. And...
% Social Sciences vs. other sciences...
% Web of causation, 
\fi



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